Abstract
We consider the problem of finding the most frequent elements in the data stream model; this problem has a linear lower bound in terms of the input length. In this paper we obtain sharper space lower bounds for this problem, not in terms of the length of the input as is traditionally done, but in terms of the quantitative properties (in this case, distribution of the element frequencies) of the input per se; this lower bound matches the best known upper bound for this problem. These bounds suggest the study of data stream algorithms through an instance-specific lens.
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Kumar, R., Panigrahy, R. (2007). On Finding Frequent Elements in a Data Stream. In: Charikar, M., Jansen, K., Reingold, O., Rolim, J.D.P. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2007 2007. Lecture Notes in Computer Science, vol 4627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74208-1_42
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DOI: https://doi.org/10.1007/978-3-540-74208-1_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74207-4
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